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After completing this reading you should be able to:
- Show how a decision tree is constructed and interpreted.
- Describe how ensembles of learners are built.
- Outline the intuition behind the K nearest neighbors and support vector machine methods for classification.
- Understand how neural networks are constructed and how their weights are determined.
- Evaluate the predictive performance of logistic regression models and neural network models using a confusion matrix.